Posts Tagged ‘bad colors

Today’s effort comes to me via friend and colleague Richard Donohue, who let me know about the good people of Ledge Wind Energy. You see, Ledge Wind Energy wants to build a wind farm in Brown County, Wisconsin. As part of this process, they filed many, many documents with the Wisconsin Public Service Commission, which you can read for yourself if you visit the linked site and then click on the link for Ledge Wind Energy (seems to be no way to link it directly).

Among these filings were a whole series of maps, one of which I’d like to focus on.

From Appendix W of Wisconsin Public Service Commission filing 9554-CE-100

Above is a map from Appendix W, showing the noise levels the wind farm is expected to generate. It was prepared for Ledge by Michael Theriault Acoustics, a noise control consulting firm in Maine.

Detail of noise map

This is not the most attractive cartographic product. It’s full of bright colors and high contrasts; it sits on top of a busy base map and features a cluttered, haphazard look. But why does this matter? The map gets the information across; does it matter if it’s devoid of aesthetic appeal?

Yes, yes it does. Wind power is a contentious issue, and if you look at the PSC website, you’ll note that citizens made hundreds of public comments on the proposal, many of them denouncing it. Every document that Ledge filed was scrutinized by members of the community who had to decide whether to sign on to the project or to try and stand in its way. And as they turned each page they saw maps which unfortunately looked like the above. Ledge Wind Energy has taken their community and made it look ugly. This doesn’t look like a happy future; it looks like a noisy one with garish colors. It doesn’t look like a map of a place I would want to live, and so it makes me want to oppose the company that’s trying to bring about the scenario depicted. This map is Ledge Wind Energy telling the people of Brown County that they’re going to be besieged by giant blue and purple amoebae.

It’s appearance is amateur, and that subtly makes Ledge look amateur. If they don’t take care to hire someone to make a quality map, one that’s legible (note the numbers in the image above) and isn’t unpleasant to look at, can they be trusted to put up heavy machinery in my town? Where else are they cutting corners? Companies that want to be seen as doing quality work need to do it not just in their main line of business, but in everything that’s got their name on it. All of it figures into how we assess them.

Most of the effort here was clearly devoted to the data, rather than the representation. This is raw GIS output; it’s designed more for the computer than the human. I’m sure many of the citizens of this area take pride in their community. This map’s appearance tells people that Ledge considers their homes to be points in an analysis. The land they live on, the land their ancestors lived on, is just something that needs to be fed into a computer. It’s quietly dehumanizing, which is a poor way to win people over to your vision. We’re used to maps like this, sure, so it’s not a conscious affront. But consider it in contrast to a better designed alternative that suggested the landscape is more than just data.

This map is a missed opportunity. This was Ledge’s chance to show people an attractive future. Imagine if more attention had been paid to aesthetics. Subtler colors that actually go together harmoniously. Show the noise polygons, but give them a less jarring, threatening color scheme. A cleaner, less cluttered style. Make the community look good; make the people there feel good when they see their community being represented. Ledge could have shown the people of Brown County that they care about doing quality work, that they care about being a partner in building a beautiful community. That these people are more than just numbers. Think of how few people make maps of this rural area, and how much goodwill Ledge would have generated by giving citizens a rare series of lovely maps of the places they care about. It shows knowledge of the community; an investment in it.

This map and the many others of similar quality which Ledge filed did not stay confined to the halls of government; they’ve been seen by the residents of the project area. Brown County Citizens for Responsible Wind Energy, for example, makes use of several of Ledge’s maps as part of their effort to stir up opposition to the project. Here’s their version of the noise map, to which they added a subtle Google Maps base and a few road names.

These maps are most definitely out there, and it seems like a poor marketing move to spend so little effort on the design of something that’s part of how Ledge interacts with the community.

Beyond the value of good aesthetics, a few other quick points are worth making about the noise map. First off, it has a pretty weak visual hierarchy. The noise polygons compete with the yellow dots which compete with the green parcels and the red numbers. Everything stands out equally, which means that nothing is prominent. I don’t know where to look first. No one is telling me what’s important or visually suggesting an order in which things should be read. I can’t focus on one type of data without being distracted by another. Everything screams for attention with bright colors in a sensory assault. Arranging things in a visual order, with the noise polygons being most prominent, and the houses just behind, and everything else faded into background, would help significantly.

Oddly, there are two legends on this map, and one is entirely verbal. The parcel boundaries and the green squares are described in a visual legend by the lower left corner. Below that is a written description of what the yellow dots mean and the blue and purple colors. Seems like those items ought to go into the visual legend, where people can compare what they see on the map to its meaning, rather than having to trying and imagine it based on description.

Lastly, I’ll point out that, under the noise polygons, you can’t actually tell which parcels are green and which parcels are not. It is an obvious waste of time and effort (both the mapmaker’s and the reader’s) to put data on the map and then not actually make it legible. Again, the map seems ill thought out. It looks sloppy, and this does not cast Ledge in a positive light.

I’m sure that Ledge Wind Energy asked their contractors to put together some quick technical maps on a tight budget. I do not fault the people who intended to simply generate a data visualization to answer a question for a regulatory filing. In my mind, though, Ledge missed an opportunity to help their cause by skimping on the design budget and not thinking past the data.

Hello, everyone. Apologies for my absence — I was challenging John Krygier for the “most dormant map blog” award, before surrendering to his superior non-updating skill. Actually, I’ve just been occupied with my classes and work at UW-Madison, as I prepare for them to evict me from the security of graduate school and into the world of “unemployment.”

I found a copy of today’s map sitting in an office in the UW Geography Department. It’s a world wall map, about 36″ x 20″, and is marked as being produced by Portal Publications, Ltd, of Novarto, CA. They appear to have folded in 2008, after renaming themselves in 2007 to Innovative Art.

Published by Portal Publications, date unknown

Mercator. On a world reference map. I’ve said this before, but I will repeat: Mercator is great if you’re a navigator, especially one in 16th century Europe. Its usefulness pretty much stops there. This map is not intended to be used for sailing from Japan to Taiwan, so I’m pretty sure another projection is called for.

The colors. Oh, the colors. You see, a lot of world maps have what you call hypsometric tinting — colors that indicate elevations. You’re probably familiar with the classic scheme of green lowlands, transitioning to yellows and oranges and browns as elevation rises, and finishing off with white for the mountaintops. On the other hand, I am wholly uncertain as to what is going on with the colors on the map above. The lightest areas are in northeast Russia, which is certainly not the tallest place in the world. So, perhaps instead of elevation, the colors are depicting land cover, and showing a snowy Russia? But then, why is northern Canada marked with verdant plains (in an area labeled on the map as “barrengrounds”)? And why is Europe completely free of green areas? It’s certainly got areas as lush as the east coast of the USA, which is marked green on this map, and it certainly has lowlands at the same elevation as other places which are marked green. I cannot make heads or tails of the color scheme, and there’s no key included. It’s like the uncanny valley of hypsometric or land-cover tinting — it’s close enough that it looks normal when you walk by without thinking, but then when you actually have a look at it, it starts weirding you out. I was going to tag this map as being unkind to those with color vision deficiencies, but I think it’s also unkind to people with normal color vision, so I’ll skip it.

While we’re on the subject of elevation, have a closer look at Asia:

Asia detail

Tibet, over in the lower right corner of the image, has some of the world’s tallest mountains, included Mt. Everest. You may have heard of it. However, on this map, it looks relatively flat compared to the giant peaks of west and central Russia, which are apparently about 30 miles high if you are comparing with Tibet. This may be because of the tinting again, and/or because of the hillshading, but I would not rely on the terrain representation to have much connection to reality. Unless you need a guide to a parallel Earth where most of Turkey is higher than Tibet.

That being said, I do kind of like the highly generalized, faceted appearance the terrain has. It’s not unnecessarily detailed, and lends a sort of “crystalline” aesthetic — almost like the map was carved out of rock, and then smoothed out a bit. Kind of like the actual Earth. I think it’s well generalized for the purpose.

Before I move away from this image, notice the Aral sea, in the center. It’s a different shade of blue than every other body of water on the map. A much brighter blue. The only thing I can think of is that perhaps the artist had some outdated data on the Aral Sea (which has changed in size and shape considerably over the past few decades), and had to replace it at the last minute and somehow they couldn’t match the colors. It has an almost pasted-in appearance.

On to labels…

Europe detail

The labeling here is a vast mess of black ink.

On the image above, I count seven types of labels: Countries, capital cities, other cities, islands, individual mountains, mountain ranges, rivers, and seas. These very different things are being represented by very similar looking labels. It’s hard to see the label for Spain and the label for Serbia as being the same type of thing, and as being separate from whatever is represented by the label for “Rome,” for example. The label styles aren’t creating a hierarchy or establishing distinctions. Now, I know a little about Europe, so I know that Croatia and Italy are both countries. But if I knew nothing of Europe, this map would be pretty confusing, and I imagine I would be left wondering, in some cases, whether a label represented a country or a city. Look at Serbia and Belgrade — it looks to me like Belgrade is the country and Serbia the city. Using multiple colors for the labels would help, as would using something like small caps for the countries, or even another typeface. Admittedly, the terrain colors limit the options for label colors, which is perhaps another strike against them.

Speaking of countries, where are the borders for Poland? Or Switzerland? They’re pretty hard to see, both on the scan and in the print. The map labels all the countries of the world (which helps date it to the early 1990s, as well), but the borders rarely show up clearly. If it’s worth putting on the map, it’s worth putting on the map in a way that people can actually see.

The labeling would also be less confusing if it were not so dense, which means that it’s hard to associate a label with a specific feature, or place them very clearly and cleanly. I’m not sure why there are so many cities on this map. It’s nice to have them for a general reference map, but I imagine it would still look fine on your wall if it was missing Arad, Romania. Time to cut some out place out — simplify, to make things easier to read. You reach a point where adding more information simply obscures information already on the map, having a net effect of making your map less and less useful as you pile more data onto it.

Can you find the label that says “Alps”? I spent quite a time figuring out what the letter “A” was doing by itself in France, before I realized that it was part of a word that was so spread out that my brain stopped processing it as one word. While tracking out labels to mimic the area of a feature is a useful practice, it can be taken too far. Especially, again, if you’re trying to educate people who are unfamiliar with the relevant geography. If I didn’t know where the Alps were, or what they were called, I don’t think this map would tell me.

I’ll also briefly mention that I looked at a few cities in the USA, and their city dots are way off in their placement, so I wouldn’t necessarily trust it to point out where some of these places actually are.

One Nice Thing: I like their use of Tanaka’s illuminated contours:

Arguably, you can criticize it for its stair-stepped appearance, but I find the aesthetic interesting and attractive. It’s still requires painstaking manual effort, so it’s still rare, unfortunately. It’s rarer, still, in my experience, to see it on something other than a greyscale background. I think it does a nice job of bringing out the continental shelf.

Also, don’t know what’s going on with the little white trapezoids in the north. I think they represent the frozen nature of the north pole.

Special thanks to Jaime Stoltenberg at the Arthur Robinson Map Library for running this through the large-format scanner for me.

(Editor’s note – Continuing our series of getting cartographers to publicly criticize themselves, we next feature Mr. Andy Woodruff, proprietor of Cartogrammar and an alumnus of the famed University of Wisconsin Cartography Lab. If you’re interested in following the brave example of Mr. Woodruff, and Mr. Reynolds before him, and showing the world some of your own cartastrophes, please write me at cartastrophic@gmail.com. — DH)

Animated maps can be a delightfully cartastrophic realm, rife with dizzying excessive motion, poorly or over-designed interfaces, annoying sound effects, and (I really, really hope) perhaps a few dancing bananas. Daniel will perhaps be wise to steer clear of them here unless he is willing to give up the rest of his life to unearthing all the bad animated cartography on the internet. With this post I will only lead this blog to test the waters gingerly.

This is a map I completed for a class called Animated and Web-based Maps, instructed by Professor Mark Harrower, the king of cartography at the University of Wisconsin-Madison, and, I should add, a known expert in map animation. It’s animated, sure, and you can click the image to load and play it, but that’s not entirely necessary for what follows. Very briefly, a bit of background on the map: it was made for a lab assignment in which students were provided with a set of county-level agricultural data for the state of Wisconsin for the years 1970 to 1999 and instructed to make an animated choropleth map of a variable of their choosing.

Typically, my first reaction to viewing this map is to vomit at the sight of the colors. Unlike many of the maps that make their way onto this site, mind you, I actually used an appropriate color scheme: a diverging scheme using official Cindy Brewer ColorBrewer specs (and it’s even colorblind-friendly!). But this red-blue scheme combined with the interface color pair of green and more disgusting green… well, I hope you’re reading this on an empty stomach. Otherwise I probably owe you a new keyboard. Oh, and if your eyes aren’t completely filled with blood yet, you just might discern in the background the ghost of a photo I took in Wisconsin’s Driftless Area.

But chalk all that up to bad taste. On to the actual cartographic crimes.

First, the white color in the classification scheme, labeled “0% or No Data.” Hold on a second, 2006 self, there’s a big difference between those two! Zero is a legitimate data value that fits in the classification scheme. “No Data” is a different animal entirely. Ideally one would avoid an incomplete data set in the first place, but sometimes that’s what you’ve got (like when that’s the data provided for the assignment). In those cases, areas without data can’t be indistinguishable from areas with data, or else the map reader can never really know what’s going on. Look at the screenshot: which counties have no data, and which have a value of zero? Impossible to know. In reality perhaps some of those counties should be off-the-charts red, but you’d never know which ones. The counties without data need to be shaded with a color that is not in the map’s color scheme, probably some kind of gray. Worse, in the image above there is in fact only one county with no data, but guess what the tooltip says when you mouse over it. That’s right, “0%.”

Next, two points about the choice of variable to map, beginning with the description I wrote near the legend:

“This map shows the change in the percent of land in farms over the preceding year in each Wisconsin county from 1970 to 1999. This is a percent change of a percent– for example, a change from 50% land in farms to 48% would be shown on the map as a -4% change ( 48 is 96% of 50), not a 2% change.”

It’s as though I deliberately chose the most complicated variable possible, probably in an effort to confuse the TA into giving me a good grade. Though I clearly realized the potential confusion (hence the descriptive example), what I didn’t even think about until now is that the exact same thing could have been mapped without any of the “percent of a percent” nonsense. The percent of land in farms for a given year is the total land area in farms divided by the total land area of the county. The percent change that I mapped is that percent for the first year minus the percent for the next year, divided by the percent for the first year. But ignoring, say, erosion on the lakeshores (and I’m sure this data set did ignore it), the total land area doesn’t change from year to year. So total land area magically cancels out of the whole equation, and it would be mathematically equivalent, and a lot clearer to the reader, to simply show the percent change in agricultural land area. I haven’t taken a math class since high school, and maybe it’s starting to show.

It's mathemagical!

Beyond that, out of all the options this choice seems like a particularly strange thing to map in an animation. The whole purpose of an animated map is to show change over time. But the data are already showing change, so now we’re dealing with change in change. Watch the animation; do you get anything out of it? I sure don’t. Yes, you can see that some years are calm and some are not, but it is very difficult to get a sense of the overall trend of what’s going on with farmland in Wisconsin. It would have been a lot clearer to just map the percent of land in agriculture and watch how that changed over thirty years. Now, this point is debatable because animating change maps is not unheard of. I’ve been told that some important minds and beards have investigated such animation. It can be useful for highlighting or discovering areas of instability. For general-purpose maps such as this one, however, mapping a change variable is best left to single, static instances. If I had animated just the percent of land in farms, the same trends could have been discerned through the animation, and the user would also actually learn something about the amount of agricultural land. A more appropriate use for the change map might have been a single map showing the change over the thirty year span. In fact, the subtitle here, “Change in percent land in farms 1970-1999″ could be realized by that single map.

For a final quarrel, I would argue that the counties on this map should have been labeled. There is ample space, and whereas you might get away with not labeling states in a US map, few people know Wisconsin’s 72 counties by heart. Instead of labels on the map, I forced the user to move the mouse cursor over a county to see what its name is. The rule by which I now try to abide is: don’t lean on interactivity to solve all cartographic challenges. Interactivity as a means to reveal data is a good way to add lots of additional information to a map, but it can also make it easy to be lazy. Laziness is for the map reader, not the map maker. If the information is useful and can be accommodated without relying on interaction, then do it. The specific data value you see when hovering over a county is a good use of interactivity for extra information; the county name is not. It’d be a lot less work to visually scan persistent labels that are sitting there on the map than it is to mouse over counties to see their names one at a time.

And an extra special bonus typographic nitpick: I misused hyphens in place of both an en and an em dash in the subtitle and description, respectively.

One Nice Thing: The animated and interactive features of the map are nearly—but not quite—unbreakable. I won’t mention the one bug I did find recently.

My last post generated a few comments from readers out there who disagreed with some of my assessments, and I wanted to start off today by mentioning that I appreciate hearing other people’s opinions on these things, and that I hope you will all continue to weigh in whether you agree with me or not. On further reflection, I think I was perhaps unfair in some elements of my critique last week. But, I have been ill for the past while, and so I’ll just pretend that my condition impaired my judgment. Of course, I’m still a bit ill now, but we’ll try to avoid a repeat.

Today’s map was submitted by my colleague Tim Wallace, who is responsible for naming this blog. We work in a building that also houses the Arthur Robinson Map Library, which occasionally gives away unwanted materials. Tim found this one on the free map table:

Detail. Obtained from Robinson Map Library, August 2009.

The provenance is unknown – it’s printed on thin magazine paper with a torn edge, and the reverse side contains portions of two articles which don’t identify the publication, though the corner reads “September 1979.” On the off chance you happen to know where it comes from, please write to me at cartastrophic@gmail.com.

I found the logic behind the legend confusing for a good while until I noticed the numbers. It appears that we have a map here which shows seismic risk for various tectonic plate boundaries. Red is the highest seismic potential. A fine-grain black-and-white checkered pattern is the lowest. Peach and yellow are in-between. This seems to come up every week on this blog, but I’ll say it again: if you’re showing ordered data, like high-to-low seismic potential, use an ordered set of symbols (colors, in this case). This is one reason why the legend threw me. Areas marked “Plate motion subparallel to arc” are apparently of a moderate-to-low seismic potential. But, because of the fact that they use a checkerboard pattern, and because I hadn’t the damnedest what that phrase meant, I couldn’t tell that item #4 on the legend was part of a larger scheme. This is worse than just misuse of colors; patterns are being thrown in needlessly now, too.

I could, in fact, still be reading this whole legend wrong, and reflecting poorly on the institution that agreed to award me a bachelor’s degree a few years ago. Feel free to comment if you think you’ve got a more sensible interpretation than my idea of items 1-6 being part of an ordered scheme of seismic potential.

One final note on the colors/patterns: The legend does not explain what the white bands are.

On to the point features. The symbols for successful forecast (presumably explained in the article) and active volcano are overprinted directly on top of the other colors. Look again at the colored bands. The red or yellow appear no different when they are on land vs. on water. The printer simply put these colors directly onto the white paper. But look now at those two point symbols – notice how their color changes based on whether they’re sitting on land or water or on top of something else. The printer put purple ink on top of green or blue or whatever was already there, instead of leaving a white space, as they did for the bands. Not sure what happened there, though there may be a reasonable explanation that someone more familiar with late 1970s printing technology can give. It does make the points very hard to see in some areas – I originally counted four stars, but now I can find eight. It also means that the point features shown in the legend do not match the color found on the map.

I’m hoping the magazine article makes the meaning of the Tsunami symbol clearer. Is this map showing Tsunamis that happened in the last decade? Ones happening right now? Not sure.

Note that the legend refers to various filled areas as being “sites” of earthquakes. Why are these not point features? Earthquakes have an epicenter, and move more in a circular outward fashion than a wide lateral band fashion. There may be more going on, as far as data processing goes (and, again, I wish I had the article that accompanies this), but it’s perplexing. Maybe the author(s) went with bands because it’s easier to see the bands than to dig out information out of scattered points? I’ll not be too hard on this, because it’s more mysterious than bad, without information to help understand why the map author(s) may have done this.

There are exactly two labels on the main map: Oaxaca, and Gulf of Alaska. Maybe those are both significant in the article, but it seems very strange to see just those two. They should probably be set in different type, at least, so that Oaxaca doesn’t look like the name of a sea off the Mexican coast. As a general guideline, cities and bodies of water ought to look different. One of the reasons for labeling things is to help readers who don’t already know what or where these features are. It’s entirely possible that a reader out there actually did look at this and, never having heard of Oaxaca, thought it was a water feature.

A similar problem comes up in the inset. Mexico is set in the same type as Central America. Central America is not (and was not), last I knew, a country. I’m reasonably sure Mexico is, however. But look at how they’re labeled – as though the text symbols mean the same thing in each case: country. And, of course, the tectonic plates are also set in the same type as everything else. Perhaps the mapmaker had a sponsorship deal from the makers of the typeface (I am having trouble identifying exactly which it is, on account of the scan resolution looking at the actual physical document, it appears to be Helvetica). If you are a typeface designer and want to pay me more than I deserve to use your glyphs on my maps, please contact me.

The inset would be better off having some kind of marker to show where exactly it corresponds to on the main map. Perhaps this might explain why Mexico was labeled: to help the reader locate the inset.

The water on the inset is jarring -the white makes it stand out far too much, calling your eye away from the main map. Best make it blue.

Boy, sure would be nice to have a legend to explain what’s going on with the inset. Are those blue triangles historical volcanic eruptions, or maybe earthquakes? Maybe they’re places less interesting than the Cheese Factory. And what are the little round-ish zones drawn in blue, which makes them hard to notice?

If you run this map through a filter which simulates how it might look to a person with the common red-green color vision impairment, you may notice that the green for the land and the orange for seismic potential level 2 end up looking very similar, which is rather problematic if you want to know which areas are plain land, and which areas might kill you in an earthquake.

A final reiteration of the main caveat to these criticisms – the original context for the map is missing, and the magazine article which I hope accompanied it may have helped this whole thing make more sense, and explained some things which seem out of place.

One Nice Thing: Some may disagree with me and say it’s overgeneralized, but I kind of like the simplicity of the linework. I think it works here, giving it an accessible, non-technical aesthetic. Michigan is misshapen, but I’ll live.

Another Nice Thing: Tim thinks it has a nice Schoolhouse Rock sort of feeling to it. Which is another way of getting at what I was saying above.

Remember David Wilkins, former US ambassador to Canada? Well, if you do a Google search on him, this map from whitepages.com comes up near the top, showing the distribution of telephone directory listings matching his name:

Since they apparently generate these automatically for most any name, I thought of doing my own. But, I figured that I would take another opportunity to increase the fame and internet profile of Mr. Wilkins. Can’t pass that up.

The colors are certainly less than ideal – as with so many of the maps seen here, there’s a mismatch between an orderable data set (number of listings) and an un-orderable symbology (the colors chosen to represent those numbers). Though, I suppose one can see a weak progression in the colors, depending on your perspective. But it’s still far from a good match to the data. Running from a light to a dark blue would be perfect. It would also be more friendly to people with color vision impairments.

It would also be nice if I didn’t have to assume that white means zero listings, since it could also reasonably mean “no data available.” Troubling is the fact that some of the small states are filled in with white on the main map, but on the inset, where they are enlarged, they are given a color. The inset needs to be consistent with the main map – else it makes it harder to understand that the inset is, in fact, a zoomed-in version of the main map.

A sacrifice made with a classed choropleth map like this is that you lose some precision in getting the numbers off of it. Look at the states in light blue – they all have anywhere from 1 to 11 listings for “David Wilkins.” Grouping states like this is perfectly reasonable, to help reduce the number of colors used on the map and make it easier for someone to pick out one distinct color and match it to the legend. Some ambiguity is necessary as part of this process. But, look at Texas – the only state colored in dark red. It apparently has anywhere from 43 to 53 listings. It’s the only state in its class – why is the exact number not specified?

The classification scheme in general is a bit odd. There are a few big goals you want to try and go for when deciding how to group your states. One is to minimize intra-class differences – that is, keep the class sizes small. You don’t want a class that goes 1 to 11 listings, and one that goes 12 to 500 listings. The second one is way too broad. Another is to try and make each class roughly the same size, which this map has a problem with. There’s one state in the dark red class, two in the orange class, and twenty-five in the light blue class. A third goal for class breaks is to try and have class breaks that are relatively even in number – as an astute reader points out below, the class breaks change in size just a bit, though they’re roughly pretty even, so I think they hold up pretty well. There are a few other goals, but I’ll leave it at that. As you might expect, it’s hard to fulfill all the goals at once, but the severity of the difference between 1 red state and 25 light blue ones is still pretty bad. The two lowest classes cover most of the country, and the two upper classes cover only three states. It makes those three states stand out, but more than they should. There’s not a large, unusual, and worth-pointing-out difference between the upper and lower end states, to my mind.

These data should probably be normalized, as well. Consider Texas again: a lot of people named David Wilkins live there. This is probably because a lot of people live there in the first place – it’s one of the most populous states. More populated places will probably have more people named David Wilkins. Likewise, you can’t find anyone named David Wilkins in places like Wyoming or South Dakota, because approximately no one lives in those states. The pattern shown by this map is highly correlated to the population distribution of the United States. It does not show whether or not people from Texas are more likely than people from Wisconsin to be named David Wilkins. Instead of making a map of how many telephone listings there are in each state for David Wilkins, the author(s) should plot how many listings there are for David Wilkins per million inhabitants of the state. Then you would find out that Delaware has 8.1 listings for David Wilkins per million inhabitants, vs. only 2.2 for Texas. The name is also particularly popular in South Carolina, which state the Ambassador calls home.

I find it a bit odd that they have region names listed for New England and the Mid Atlantic, but not the rest of the country. Also, I was under the impression that Maine was part of New England.

One Nice Thing: Those inset maps to the right sure are handy.

With that, I will leave off today’s effort to make this blog the #1 item on a Google search for David Wilkins.

Today’s effort is brought to my attention by one of our readers, Robin, who previously alerted me to the problems of Moon Maps. This map appears in a post on the OKTrends blog, where the authors analyze the geographic distribution of how people answer various questions on the dating site OKCupid. We’ll take one of them as an example:

Side note: the darkest red, for the highest percentage “No,” shows up as a single pixel at DC. So, I cannot claim that the map doesn’t follow the legend. Though, the DC dot is so unnoticeable it might as well not be. Putting an area on a map which is too small to be seen is a serious problem. Better for the authors to include an inset or two, showing the smaller states in a more magnified fashion (as we say in the business: at a larger scale).

There’s a fundamental error that a lot of people are prone to making with these maps (and, in fact, I didn’t notice myself doing it until I’d spent quite a while looking at these). If you look at the map above, or any of the others in the OKTrends post, there is always one state at each end of the scale. In the above example, Nevada is the brightest green, matching the far “Yes” end of the scale, and DC (thought you can scarcely see it) is the darkest red, matching the “No” end of the scale. This does not mean that 100% of the people in Nevada answered yes and 100% of those in DC said no. It just means that more people answered yes in Nevada than anywhere else. Maybe in DC 2% of the population said “yes,” and in Nevada 5% did, with all the other states in between. But this legend makes it appear as if DC uniformly said “No,” and Nevada “Yes,” with the vote being split in all other states. It is unintentionally misleading, which is the most tragic cartographic sin. The authors wanted to convey some information in good faith, but their communications became twisted and false.

The Yes-No labels on the legend are not the only problem. It’s the color scheme. It’s a diverging color scheme – the green and the red are opposite ends, and states shade toward one end or the other. There are many good reasons to use these schemes, but in this instance, it just makes it look like there are solid No states and solid Yes states, instead of states where a handful of people said yes, and those where a slightly larger handful of people said yes. The color scheme should be one hue, changing in lightness (say, from a pink to a dark red), and the map should show % yes votes (or % no) votes only. This still lets you pick out trends – Nevada has more Yes votes than other states, but it doesn’t mislead you in to a panic about the amoral citizens of Nevada and how they disregard human life. You can tell that “yes” is unpopular, but less so in certain areas.

Another reason to ditch the red-green scheme, which the authors use uniformly throughout their maps: It doesn’t always make sense when the options are other than yes and no. Here’s a map from later in their post:

These two colors, green and red, have certain connotations of positive and negative in much of the english-speaking world, so here, the scheme is a problem, unless you’re going with an implied value judgment where “Right to Bear Arms = Good” and “Right to Vote = Bad.” The people on the OKTrends blog don’t quite strike me as that type. Nor the type to greenlight an unequal valuation of human lives. And, of course, the legend does again make it look like Idaho is 100% behind giving up the right to vote. I’m guessing (and it is just guessing) that if you looked at the data, the vast majority of respondents in each state said they’d rather keep the vote and lose their guns. But Idaho happened to have the most people who were more fond of guns than voting (hard to hold that against them, given the political climate). Probably just a few percent. But people will come away from this map with the sentence stuck in their mind “Idaho strongly prefers to lose the right to vote.” I have belabored the point, so let’s move on.

Note that there are two states in the lower right corner which are not actually filled in with any color. Thanks for playing, Hawaii and Alaska! It’s OK — they’re just happy to be there, given how often they’re left off of US maps.

The shapes of the states are poorly generalized, and may in fact have been drawn freehand. Possibly just traced loosely, instead. But it has that feeling to it. Many of the states look slightly-to-moderately wrong. Look at my beloved Michigan – it’s become an amorphous blob. Saginaw Bay is entirely missing. It’s a sizable body of water. Like, nearly as big as some states. Also, I’m pretty sure Ohio doesn’t look quite like that. And Maryland seems to have taken over part of Virginia. Wyoming, though, is just as rectangular as ever.

I would be tempted to complain about the map projection, but since I think it’s freehanded, it doesn’t exactly have one.

Note the line around Michigan, showing the water border between the US and Canada as it passes through the Great Lakes. Except, the line only shows the border as it passes through two of the lakes. The actual border passes through two additional ones, north of Ohio, Pennsylvania, and New York – but you don’t see that section here for some reason. So, we have part of a Canadian border. And no Canada, by the by.

The map suffers from the dreaded island effect, where the US looks like it’s floating off in the ocean with no land nearby, and Canada looks like a great and forbidding sea, much like the Gulf of Mexico or the Atlantic Ocean. I might not call this a problem for this map otherwise, but putting a portion of the Canadian border in, and then not actually drawing Canada, makes this a serious issue.

Nitpicky: Both maps have a few artifacts here and there, like in Louisiana in the bottom map. A few pixels the wrong color.

One Nice Thing: It’s an unclassed choropleth, which I rather like. Most choropleth maps organize data into classes – say, one color will represent all states with yes votes between 0% and 5%, and the next color will be for states from 6% to 10%, etc. — the states are grouped. In an unclassed choropleth, such as those above, each state is given a color in exact proportion to the number of votes for one choice or the other. A state with 1.1% yes votes gets a different color from one with 1.2%, because they have different values. They’re never grouped together into the same color, as they would be in a classed choropleth. There’s a whole debate as to which is better, classed or unclassed, and I will not get into it here. I will just say that I am a fan of the unclassed choropleth, so I think it’s a Nice Thing.

Addendum: I am a bit embarrassed that I didn’t notice this at first, but these maps are very unkind to the millions of people with red-green colorblindness, as several commenters on the original OKTrends post mentioned. Here’s an approximation of what a red-green colorblind person sees when they look at one of these maps:

(Editor’s note: Daniel Reynolds is a colleague of mine in the University of Wisconsin Cartography Laboratory. He has graciously agreed to act as a guest poster, publicly criticizing some of his own works for your edification and entertainment — DH)

Okay, so the title is a lie. This wasn’t my first map. Or even my second. But this was the first map I made where I wasn’t trying to copy a lab step by step. I cringe every time I see this thing.

In my introductory cartography class at Utah State University, the first assignment was to do a combination choropleth and dot density layout with the given data in ArcView 3.3. Why not something else, something not quite so dated given that it was 2006? Or perhaps something with better tools to make stuff pretty? Well, the instructor had never used ArcGIS 9.1 (the then-current standard at USU) since he hadn’t been working in the geospatial field for a number of years. All that is to say that I didn’t have the best software to produce a good looking map. My excuses stop there…

When I made this map, I didn’t know much about color theory. That is unfortunate, as I’m pretty sure I picked one of the worst possible color schemes. By that, I mean the choropleth makes me want to stab my eyes out. I’m pretty sure it was a default scheme that the software spit out. Color is, in my opinion one of the easier ways to categorize a map as a ‘GIS map’ or a ‘Cartographic Map'; a good number of the default color schemes in the GIS packages I have used are passable, but need work. Other schemes make Cynthia Brewer cry…like the one I used. While it makes sense to use a diverging color scheme to highlight areas of population growth and population decline, I managed to abuse this concept pretty thoroughly. A diverging scheme is meant to highlight data in relation to a critical value. A good diverging scheme is made of two hues changing in saturation and/or brightness as you move away from the critical value.

Instead of following this sensible approach, I used four hues (although I think at the time I felt that orange/yellow and green/blue fit together reasonably well). This results in a color scheme that could be interpreted as categorical even though the data beg for an ordinal scheme. On top of all that, I don’t think there is enough differentiation between some of the colors (the two blues, the two greens). A little less obvious is that I really goofed up the relationship between the color scheme and the critical value. The second lowest category (0-11%) should be part of the color values above the critical break. In other words, it should be some shade of green. As it is, the critical value appears to be 11% instead of the more sensible 0%. While we’re examining those values, I should probably point out that the break values were likely determined by the software without much thought on my part. My best guess is that I used a standard deviation classification. Why? I don’t know!

A few last parting shots at the choropleth map:

Not only did I fail to include surrounding landmasses to avoid the island effect, but I also managed to leave an enormous amount of empty space on the page.

The legend drives me nuts. Again, I didn’t effectively use the space at hand. I think it would be much more aesthetically pleasing if it were more compact (make the legend title two lines instead of one).

The labels could be much smaller and less distracting. They disappear on the darker states.

The topic of the map is population change, but we have no frame of reference. Is this change from 1930 to 2030?

On to the dot density map:

Part of the assignment was to symbolize the population density of hurricane-impacted states on a county level. While I think this is an appropriate scale for the map in question, I had no clue at the time. I remember the concepts of dot size versus dot value being very vague and somewhat hard to comprehend. The basic goal with a dot map is to pick a value that is easy to wrap your head around and a size that allows you to display that value effectively. I won’t go into more detail here. I managed to pick a decent value but then made the dots so small that it looks like someone tipped over a pepper shaker on my map.

My other major problem with the dot-density map is that the data set we were given is not appropriate for the given scale. The coastlines are quite jagged which is a result of using a dataset that has been generalized (poorly in my opinion) for a smaller scale. (Yes, I do mean smaller scale).

Lastly, the island effect is avoided this time around, but the fill color ended up with a bizarre and entirely unnecessary pattern. I’m not quite sure why this happened as I don’t recall choosing this. My best guess is that there was some image integrity loss since it is stored as a jpeg.

One Nice Thing: I had the presence of mind to generalize the legend values in the choropleth rather than using precise but essentially useless decimals.

PS. At the time I made this map, I was really proud of the background color. I had never really used image editing software (other than doodling in MS Paint) so realizing I could draw a box, make it blue, and drop it behind everything else made me very happy. Unfortunately, the particular shade I chose makes me gag.

PPS. If you are wondering why we mapped predicted population change for the entire US with population density of hurricane impacted states, I have no clue.

(Editor’s note: To avoid leaving the Internet with the impression that Mr. Reynolds still regularly makes maps worthy of such criticism, I leave you with a link to his portfolio, where you can see his current, and much better works. — DH)